Page 98 - AI Standards for Global Impact: From Governance to Action

AI Standards for Global Impact: From Governance to Action

Wind River explored how organizations can balance innovation with responsibility – ensuring
that AI deployed at the edge not only advances business and operational goals but does so
in a way that is safe and accountable. The Wind River solution is designed to prevent injuries
by minimizing human error and enhancing workplace safety in environments with increased
automation. Wind River’s solution comprises a solution for real-time, context-aware edge AI
which is pre-integrated with market-leading hardware to simplify the use of AI to improve
industrial operations.

This approach powers critical applications – including computer vision, sensor analytics,
industrial automation, and security – across a wide range of industries, such as manufacturing,
healthcare, logistics, and energy.
A camera monitors the manufacturing floor and the AI system analyzes the image to ensure
workers are equipped with the correct safety gear, such as hard hats and vests. When workers
have the proper protective equipment, the system enables robots in the area to become
operable. If someone removes a hard hat, for example, the system detects the change and
instantly stops robots from operating.

13�4�5 From classroom to community: Five years of TinyML academic network
impact

Lessons learned from building and scaling the network of tinyML community highlight how
collaborative efforts among educators, students, and industry partners have fostered innovation,
inclusivity, and real-world impact. Challenges faced, such as resource limitations and diverse
educational contexts, were addressed through partnerships and open-source approaches.

13�4�6 AI inference chip
AI development is typically divided into two stages: training, which demands massive datasets
and compute, and inference, where trained models are deployed to solve real-world problems.
As AI adoption broadens, cloud-based inference is quickly taking centre stage. According to
the International Data Corporation (IDC), cloud-based inference accounted for 58.5% of AI
computing power in 2022 and is projected to hit 62.2% by 2026. It is forecasted that AI inference
compute demand will grow over 80% annually, potentially surpassing training as the primary
driver for data centre expansion.

Intellifusion developed AICB (AI Computation Block), an innovative architecture for edge AI
which is enabling AI inference chips to meet the computational demands of various devices.
The diagram below shows the evolution of Intellifusion’s inference chips.

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